Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae

Q1 Chemical Engineering
Hegazy Rezk , Ali Alahmer , Abdul Ghani Olabi , Enas Taha Sayed
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Abstract

Enhancing biohydrogen production from microalgae is crucial in addressing environmental and energy challenges. It provides a sustainable, clean energy source while reducing greenhouse gas emissions. Moreover, it advances microalgae-based biotechnology, enabling innovative biofuel production and ecological revitalization. The main target of this study is to develop a robust ANFIS model to simulate the biohydrogen production process from microalgae within photobioreactors. The study focuses on enhancing hydrogen yield by optimizing three critical process parameters: sulfur concentration (%), run time (hours), and wet biomass concentration (g/L). Initially, an adaptive neuro-fuzzy inference system (ANFIS) model for biohydrogen production process is constructed based on empirical data. Subsequently, the red-tailed hawk algorithm (RTH) is used to determine the optimal values for the process parameters, corresponding to maximum hydrogen yield. The performance of ANFIS model in predicting hydrogen yield is assessed using root mean square error (RMSE) and coefficient-of-determination (R2) values. The obtained RMSE values for training and testing data are 2.8477 × 10−05 and 1.2807, respectively, while the corresponding R2 values are 1.0 and 0.9911 for training and testing. The introduction of fuzzy logic into the model significantly improves its predictive accuracy, as evidenced by the drop in RMSE from 10.79 with ANOVA to 0.7159 with ANFIS, representing a substantial 93.4 % decrease. The remarkable precision of the ANFIS model, indicated by its low RMSE and high R2 values, underscores the success of the modeling stage. The combination between ANFIS with the RTH technique yields impressive results, leading to a hydrogen yield enhancement of 6.87 % and 26.65 % when compared to both measured data and ANOVA.

Abstract Image

应用人工智能和红尾鹰优化技术提高微藻生物制氢能力
提高微藻生物制氢能力对于应对环境和能源挑战至关重要。它提供了一种可持续的清洁能源,同时减少了温室气体排放。此外,它还推动了微藻生物技术的发展,实现了创新生物燃料生产和生态振兴。本研究的主要目标是开发一种稳健的 ANFIS 模型,用于模拟光生物反应器中微藻的生物制氢过程。研究重点是通过优化硫浓度(%)、运行时间(小时)和湿生物质浓度(克/升)这三个关键工艺参数来提高氢气产量。首先,根据经验数据构建了生物制氢工艺的自适应神经模糊推理系统(ANFIS)模型。随后,使用红尾鹰算法(RTH)确定工艺参数的最佳值,以获得最大氢气产量。ANFIS 模型预测氢气产量的性能使用均方根误差(RMSE)和确定系数(R2)值进行评估。训练数据和测试数据的 RMSE 值分别为 2.8477 × 10-05 和 1.2807,而相应的 R2 值分别为 1.0 和 0.9911。将模糊逻辑引入模型后,其预测精度得到了显著提高,RMSE 从方差分析的 10.79 下降到 ANFIS 的 0.7159,即大幅下降了 93.4%。ANFIS 模型的 RMSE 值和 R2 值都很低,这表明 ANFIS 模型非常精确,突出说明了建模阶段的成功。ANFIS 与 RTH 技术的结合产生了令人印象深刻的结果,与测量数据和方差分析相比,氢气产量分别提高了 6.87 % 和 26.65 %。
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来源期刊
International Journal of Thermofluids
International Journal of Thermofluids Engineering-Mechanical Engineering
CiteScore
10.10
自引率
0.00%
发文量
111
审稿时长
66 days
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